Satellite remote sensing technology has the following characteristics: fast, macro, accurate, objective, timely, dynamic and so on, for large-scale crop monitoring and yield forecasting. It has a unique advantage. However, remote monitoring is often subject to spatial resolution, time resolution and other factors of remote sensing data, and most remote sensing information reflects the physical condition of the moment. Internal mechanism for crop growth is difficult to reveal by means of remote sensing. Crop growth model is a mechanism model that describes crop growth. It is a process-oriented, time highly dynamic model. It also has physical advantage. Crop model can simulate crop growth and development continuously, and give explanation of reasons and essence of environmental factor impacts on crop. Crop model used in the single-point can give the appropriate initial data and parameter values that model needs; it can accurately simulate the growth of crops and the final production process. However, when the application of crop simulation from a single point to the region, it generated some issues that some space change information can not be added to the model. In other words, it can not solve the accessing of the initially macro data and the adjusting of the parameter when the crop model is used on large-scale.   With the rapid development of crop model and remote sensing technology, their combination will be meaningful and applicable for crop monitoring and yield prediction. It is a new idea of remote sensing yield prediction put forward by international community in recent years. Accurate crop growth monitoring and yield prediction are significantly important to agricultural production. The coupling of remote sensing data and crop growth models has highly potential application to solving the above problems. It has gradually become an important field of study. Therefore, more and more research people concern it. On the basis of relevant information, this paper reviewed the coupled application of remote sensing information and crop growth model, the forcing method and the assimilation method. 
      In order to enhance crop growth simulation model of precision, the forcing method used remote sensing data to inverse the initial value of the model or used inverted value to update the output parameter values of crop growth model directly. The assimilation method adjusted the initial conditions or the value of the parameter which are closely related to crop growth and yield formation to narrow the disparity between a/some remote sensing “observation” data and the simulated value corresponding to the crop model for achieving the purpose of estimating the initial value or the parameter.
      This paper also reviewed their development process, and summed up the application of the two methods. It includes the application of these two methods at home and abroad, the situation and the effect. Satellite remote sensing and crop growth model have their own advantages in crop monitoring and yield estimation. Their combination can play their respective advantages, and have potential application value. At the end of the article，the existed problems and further research directions in the field were proposed, and the aspects that should be strengthened were summarized.